{
 "cells": [
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    "mdEditEnable": false,
    "slideshow": {
     "slide_type": "slide"
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    "tags": []
   },
   "source": [
    "## 分析某公司员工离职原因\n",
    "### 项目介绍\n",
    "- 项目人：范沛坤\n",
    "- 时间：2021年6月28日-2021年7月4日\n",
    "- 数据源：https://gitee.com/fanpeikun/python-da-final/raw/master/HR_comma_sep.csv\n",
    "- 目标：分析该公司员工离职的原因，帮助该公司找到问题的关键，以解决公司在人力资源方面亟需解决的问题。"
   ]
  },
  {
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   "execution_count": 163,
   "metadata": {
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    "id": "19A09F09F5E7468D9C92817F6DB908A7",
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    "scrolled": false,
    "slideshow": {
     "slide_type": "slide"
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    "tags": []
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {
    "hide_input": false,
    "id": "00D1325946E543468E897C0B3C52BEE5",
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    "scrolled": false,
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5.1.0\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "        <script type=\"text/javascript\">\n",
       "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
       "        if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
       "        if (typeof require !== 'undefined') {\n",
       "        require.undef(\"plotly\");\n",
       "        requirejs.config({\n",
       "            paths: {\n",
       "                'plotly': ['https://cdn.plot.ly/plotly-2.2.0.min']\n",
       "            }\n",
       "        });\n",
       "        require(['plotly'], function(Plotly) {\n",
       "            window._Plotly = Plotly;\n",
       "        });\n",
       "        }\n",
       "        </script>\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from plotly import __version__    #运用了plotly动态图表\n",
    "print (__version__)\n",
    "\n",
    "from plotly.offline import init_notebook_mode, iplot\n",
    "init_notebook_mode(connected=True)\n",
    "from plotly.graph_objs import *\n",
    "import colorlover as cl\n",
    "import plotly as py\n",
    "import plotly.graph_objs as go\n",
    "import plotly.express as px\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {
    "hide_input": false,
    "id": "0400BBCD79E446FB99EDD3ADB8376D0D",
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     "source_hidden": true
    },
    "scrolled": false,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "colors = ['#e43620', '#FFE3F1','#00C9C8','#F9F871', '#7F4D3E', '#956891','#254D4C', '#FF864B']"
   ]
  },
  {
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   "execution_count": 166,
   "metadata": {
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    "jupyter": {},
    "scrolled": false,
    "slideshow": {
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   },
   "outputs": [
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       "      <th></th>\n",
       "      <th>satisfaction_level</th>\n",
       "      <th>last_evaluation</th>\n",
       "      <th>number_project</th>\n",
       "      <th>average_montly_hours</th>\n",
       "      <th>time_spend_company</th>\n",
       "      <th>Work_accident</th>\n",
       "      <th>left</th>\n",
       "      <th>promotion_last_5years</th>\n",
       "      <th>sales</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.38</td>\n",
       "      <td>0.53</td>\n",
       "      <td>2</td>\n",
       "      <td>157</td>\n",
       "      <td>3</td>\n",
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       "       satisfaction_level  last_evaluation  number_project  \\\n",
       "0                    0.38             0.53               2   \n",
       "1                    0.80             0.86               5   \n",
       "2                    0.11             0.88               7   \n",
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       "...                   ...              ...             ...   \n",
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       "14998                0.37             0.52               2   \n",
       "\n",
       "       average_montly_hours  time_spend_company  Work_accident  left  \\\n",
       "0                       157                   3              0     1   \n",
       "1                       262                   6              0     1   \n",
       "2                       272                   4              0     1   \n",
       "3                       223                   5              0     1   \n",
       "4                       159                   3              0     1   \n",
       "...                     ...                 ...            ...   ...   \n",
       "14994                   151                   3              0     1   \n",
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       "14996                   143                   3              0     1   \n",
       "14997                   280                   4              0     1   \n",
       "14998                   158                   3              0     1   \n",
       "\n",
       "       promotion_last_5years    sales  salary  \n",
       "0                          0    sales     low  \n",
       "1                          0    sales  medium  \n",
       "2                          0    sales  medium  \n",
       "3                          0    sales     low  \n",
       "4                          0    sales     low  \n",
       "...                      ...      ...     ...  \n",
       "14994                      0  support     low  \n",
       "14995                      0  support     low  \n",
       "14996                      0  support     low  \n",
       "14997                      0  support     low  \n",
       "14998                      0  support     low  \n",
       "\n",
       "[14999 rows x 10 columns]"
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     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 读取数据\n",
    "df = pd.read_csv(\"https://gitee.com/fanpeikun/python-da-final/raw/master/HR_comma_sep.csv\")\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {
    "scrolled": true
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   "outputs": [
    {
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       "      <th>工龄（年）</th>\n",
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      "text/plain": [
       "        满意度  绩效评估  完成项目数量  每月平均工作时长（hr）  工龄（年）  有无工伤  是否离职  过去五年是否有升职  \\\n",
       "0      0.38  0.53       2           157      3     0     1          0   \n",
       "1      0.80  0.86       5           262      6     0     1          0   \n",
       "2      0.11  0.88       7           272      4     0     1          0   \n",
       "3      0.72  0.87       5           223      5     0     1          0   \n",
       "4      0.37  0.52       2           159      3     0     1          0   \n",
       "...     ...   ...     ...           ...    ...   ...   ...        ...   \n",
       "14994  0.40  0.57       2           151      3     0     1          0   \n",
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       "14996  0.37  0.53       2           143      3     0     1          0   \n",
       "14997  0.11  0.96       6           280      4     0     1          0   \n",
       "14998  0.37  0.52       2           158      3     0     1          0   \n",
       "\n",
       "          工作部门  工资薪酬等级  \n",
       "0        sales     low  \n",
       "1        sales  medium  \n",
       "2        sales  medium  \n",
       "3        sales     low  \n",
       "4        sales     low  \n",
       "...        ...     ...  \n",
       "14994  support     low  \n",
       "14995  support     low  \n",
       "14996  support     low  \n",
       "14997  support     low  \n",
       "14998  support     low  \n",
       "\n",
       "[14999 rows x 10 columns]"
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     },
     "execution_count": 167,
     "metadata": {},
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   ],
   "source": [
    "# 改变名称便于理解\n",
    "df.rename(columns = {'satisfaction_level':'满意度','last_evaluation':'绩效评估','number_project':'完成项目数量','average_montly_hours':'每月平均工作时长（hr）','time_spend_company':'工龄（年）','Work_accident':'有无工伤','left':'是否离职','promotion_last_5years':'过去五年是否有升职','sales':'工作部门','salary':'工资薪酬等级'},inplace = True)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "54338742C68F4D79A1AD44EFA5EC64FF",
    "jupyter": {},
    "mdEditEnable": false,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "source": [
    "### 检查数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {
    "hide_input": true,
    "id": "3685591B7BA14D8AB79FDFE4B29919E2",
    "jupyter": {
     "source_hidden": true
    },
    "scrolled": false,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>满意度</th>\n",
       "      <th>绩效评估</th>\n",
       "      <th>完成项目数量</th>\n",
       "      <th>每月平均工作时长（hr）</th>\n",
       "      <th>工龄（年）</th>\n",
       "      <th>有无工伤</th>\n",
       "      <th>是否离职</th>\n",
       "      <th>过去五年是否有升职</th>\n",
       "      <th>工作部门</th>\n",
       "      <th>工资薪酬等级</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.38</td>\n",
       "      <td>0.53</td>\n",
       "      <td>2</td>\n",
       "      <td>157</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.80</td>\n",
       "      <td>0.86</td>\n",
       "      <td>5</td>\n",
       "      <td>262</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.11</td>\n",
       "      <td>0.88</td>\n",
       "      <td>7</td>\n",
       "      <td>272</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.72</td>\n",
       "      <td>0.87</td>\n",
       "      <td>5</td>\n",
       "      <td>223</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.37</td>\n",
       "      <td>0.52</td>\n",
       "      <td>2</td>\n",
       "      <td>159</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    满意度  绩效评估  完成项目数量  每月平均工作时长（hr）  工龄（年）  有无工伤  是否离职  过去五年是否有升职   工作部门  \\\n",
       "0  0.38  0.53       2           157      3     0     1          0  sales   \n",
       "1  0.80  0.86       5           262      6     0     1          0  sales   \n",
       "2  0.11  0.88       7           272      4     0     1          0  sales   \n",
       "3  0.72  0.87       5           223      5     0     1          0  sales   \n",
       "4  0.37  0.52       2           159      3     0     1          0  sales   \n",
       "\n",
       "   工资薪酬等级  \n",
       "0     low  \n",
       "1  medium  \n",
       "2  medium  \n",
       "3     low  \n",
       "4     low  "
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {
    "hide_input": true,
    "id": "066C9BC008024299AF24E5262E20A3AE",
    "jupyter": {
     "source_hidden": true
    },
    "scrolled": false,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "共有 14999 条员工记录， 10 个员工特征。\n"
     ]
    }
   ],
   "source": [
    "print(\"共有\",df.shape[0],\"条员工记录，\",df.shape[1],\"个员工特征。\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {
    "hide_input": true,
    "id": "672A7F2A75894796AA4A0FB52E57C517",
    "jupyter": {
     "source_hidden": true
    },
    "scrolled": false,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "满意度             0\n",
       "绩效评估            0\n",
       "完成项目数量          0\n",
       "每月平均工作时长（hr）    0\n",
       "工龄（年）           0\n",
       "有无工伤            0\n",
       "是否离职            0\n",
       "过去五年是否有升职       0\n",
       "工作部门            0\n",
       "工资薪酬等级          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 检查是否存在缺失值\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "A3FF6C33439D4954A25978522D2B7115",
    "jupyter": {},
    "mdEditEnable": false,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "source": [
    "### 查看数据信息"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {
    "hide_input": true,
    "id": "2A96A50E9B8242A98F48BBA07516863D",
    "jupyter": {
     "source_hidden": true
    },
    "scrolled": false,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 14999 entries, 0 to 14998\n",
      "Data columns (total 10 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   满意度           14999 non-null  float64\n",
      " 1   绩效评估          14999 non-null  float64\n",
      " 2   完成项目数量        14999 non-null  int64  \n",
      " 3   每月平均工作时长（hr）  14999 non-null  int64  \n",
      " 4   工龄（年）         14999 non-null  int64  \n",
      " 5   有无工伤          14999 non-null  int64  \n",
      " 6   是否离职          14999 non-null  int64  \n",
      " 7   过去五年是否有升职     14999 non-null  int64  \n",
      " 8   工作部门          14999 non-null  object \n",
      " 9   工资薪酬等级        14999 non-null  object \n",
      "dtypes: float64(2), int64(6), object(2)\n",
      "memory usage: 1.1+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "89F7051707504B718830466C57C2119A",
    "jupyter": {},
    "mdEditEnable": false,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "source": [
    "### 展示所有类型特征的信息"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {
    "id": "DD144D3C134345498752387CC6BC89B8",
    "jupyter": {},
    "scrolled": true,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>工作部门</th>\n",
       "      <th>工资薪酬等级</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>14999</td>\n",
       "      <td>14999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>4140</td>\n",
       "      <td>7316</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         工作部门 工资薪酬等级\n",
       "count   14999  14999\n",
       "unique     10      3\n",
       "top     sales    low\n",
       "freq     4140   7316"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe(include=['O'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "C249A93037644F35941FACA086736AC7",
    "jupyter": {},
    "mdEditEnable": false,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "source": [
    "### 优化columns的顺序"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {
    "id": "BDBCF986F2EA46B483BE96E537B143D4",
    "jupyter": {},
    "scrolled": false,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reordered Columns: ['满意度', '绩效评估', '完成项目数量', '每月平均工作时长（hr）', '工龄（年）', '有无工伤', '过去五年是否有升职', '工作部门', '工资薪酬等级', '是否离职']\n"
     ]
    }
   ],
   "source": [
    "# 将\"是否离职\"列放置在最后一行便于查看\n",
    "cols = df.columns\n",
    "cols = list(cols[:6]) + list(cols[7:]) + [cols[6]]\n",
    "print('Reordered Columns:',cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {
    "id": "DADFEE79DA1646E58355C2DF956D38A8",
    "jupyter": {},
    "scrolled": false,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>满意度</th>\n",
       "      <th>绩效评估</th>\n",
       "      <th>完成项目数量</th>\n",
       "      <th>每月平均工作时长（hr）</th>\n",
       "      <th>工龄（年）</th>\n",
       "      <th>有无工伤</th>\n",
       "      <th>过去五年是否有升职</th>\n",
       "      <th>工作部门</th>\n",
       "      <th>工资薪酬等级</th>\n",
       "      <th>是否离职</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.38</td>\n",
       "      <td>0.53</td>\n",
       "      <td>2</td>\n",
       "      <td>157</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.80</td>\n",
       "      <td>0.86</td>\n",
       "      <td>5</td>\n",
       "      <td>262</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>medium</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.11</td>\n",
       "      <td>0.88</td>\n",
       "      <td>7</td>\n",
       "      <td>272</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>medium</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.72</td>\n",
       "      <td>0.87</td>\n",
       "      <td>5</td>\n",
       "      <td>223</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.37</td>\n",
       "      <td>0.52</td>\n",
       "      <td>2</td>\n",
       "      <td>159</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    满意度  绩效评估  完成项目数量  每月平均工作时长（hr）  工龄（年）  有无工伤  过去五年是否有升职   工作部门  工资薪酬等级  \\\n",
       "0  0.38  0.53       2           157      3     0          0  sales     low   \n",
       "1  0.80  0.86       5           262      6     0          0  sales  medium   \n",
       "2  0.11  0.88       7           272      4     0          0  sales  medium   \n",
       "3  0.72  0.87       5           223      5     0          0  sales     low   \n",
       "4  0.37  0.52       2           159      3     0          0  sales     low   \n",
       "\n",
       "   是否离职  \n",
       "0     1  \n",
       "1     1  \n",
       "2     1  \n",
       "3     1  \n",
       "4     1  "
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 根据排好的列表顺序应用于dataframe上\n",
    "df = df[cols]\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {
    "hide_input": true,
    "id": "19CFDA80E74D4E1B95466E5ACA1F8803",
    "jupyter": {
     "source_hidden": true
    },
    "scrolled": true,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(14999, 10)\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 14999 entries, 0 to 14998\n",
      "Data columns (total 10 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   满意度           14999 non-null  float64\n",
      " 1   绩效评估          14999 non-null  float64\n",
      " 2   完成项目数量        14999 non-null  int64  \n",
      " 3   每月平均工作时长（hr）  14999 non-null  int64  \n",
      " 4   工龄（年）         14999 non-null  int64  \n",
      " 5   有无工伤          14999 non-null  int64  \n",
      " 6   过去五年是否有升职     14999 non-null  int64  \n",
      " 7   工作部门          14999 non-null  object \n",
      " 8   工资薪酬等级        14999 non-null  object \n",
      " 9   是否离职          14999 non-null  int64  \n",
      "dtypes: float64(2), int64(6), object(2)\n",
      "memory usage: 1.1+ MB\n"
     ]
    }
   ],
   "source": [
    "print(df.shape)\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "8FDC8BFA49BD424DB4E4A3A9A5D56783",
    "jupyter": {},
    "mdEditEnable": false,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "source": [
    "## 数据分析\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "CB53B62790A64689A79BE57D860A4F09",
    "jupyter": {},
    "mdEditEnable": false,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "source": [
    "### 查看在职与离职类别下各特征的均值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {
    "hide_input": true,
    "id": "4EE4D78379BD495BAD07A1551B2E91C9",
    "jupyter": {
     "source_hidden": true
    },
    "scrolled": false,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>满意度</th>\n",
       "      <th>绩效评估</th>\n",
       "      <th>完成项目数量</th>\n",
       "      <th>每月平均工作时长（hr）</th>\n",
       "      <th>工龄（年）</th>\n",
       "      <th>有无工伤</th>\n",
       "      <th>过去五年是否有升职</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>是否离职</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.666810</td>\n",
       "      <td>0.715473</td>\n",
       "      <td>3.786664</td>\n",
       "      <td>199.060203</td>\n",
       "      <td>3.380032</td>\n",
       "      <td>0.175009</td>\n",
       "      <td>0.026251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.440098</td>\n",
       "      <td>0.718113</td>\n",
       "      <td>3.855503</td>\n",
       "      <td>207.419210</td>\n",
       "      <td>3.876505</td>\n",
       "      <td>0.047326</td>\n",
       "      <td>0.005321</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           满意度      绩效评估    完成项目数量  每月平均工作时长（hr）     工龄（年）      有无工伤  \\\n",
       "是否离职                                                                   \n",
       "0     0.666810  0.715473  3.786664    199.060203  3.380032  0.175009   \n",
       "1     0.440098  0.718113  3.855503    207.419210  3.876505  0.047326   \n",
       "\n",
       "      过去五年是否有升职  \n",
       "是否离职             \n",
       "0      0.026251  \n",
       "1      0.005321  "
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "left_summary = df.groupby(by=['是否离职'])  #运用groupby函数\n",
    "left_summary.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {
    "hide_input": true,
    "id": "C388F8BD37A6481B8E23502FF975675E",
    "jupyter": {
     "source_hidden": true
    },
    "scrolled": false,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>满意度</th>\n",
       "      <th>绩效评估</th>\n",
       "      <th>完成项目数量</th>\n",
       "      <th>每月平均工作时长（hr）</th>\n",
       "      <th>工龄（年）</th>\n",
       "      <th>有无工伤</th>\n",
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       "      <th>是否离职</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>14999.000000</td>\n",
       "      <td>14999.000000</td>\n",
       "      <td>14999.000000</td>\n",
       "      <td>14999.000000</td>\n",
       "      <td>14999.000000</td>\n",
       "      <td>14999.000000</td>\n",
       "      <td>14999.000000</td>\n",
       "      <td>14999.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.612834</td>\n",
       "      <td>0.716102</td>\n",
       "      <td>3.803054</td>\n",
       "      <td>201.050337</td>\n",
       "      <td>3.498233</td>\n",
       "      <td>0.144610</td>\n",
       "      <td>0.021268</td>\n",
       "      <td>0.238083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.248631</td>\n",
       "      <td>0.171169</td>\n",
       "      <td>1.232592</td>\n",
       "      <td>49.943099</td>\n",
       "      <td>1.460136</td>\n",
       "      <td>0.351719</td>\n",
       "      <td>0.144281</td>\n",
       "      <td>0.425924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.090000</td>\n",
       "      <td>0.360000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>96.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.440000</td>\n",
       "      <td>0.560000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>156.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.640000</td>\n",
       "      <td>0.720000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.820000</td>\n",
       "      <td>0.870000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>245.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>310.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                满意度          绩效评估        完成项目数量  每月平均工作时长（hr）         工龄（年）  \\\n",
       "count  14999.000000  14999.000000  14999.000000  14999.000000  14999.000000   \n",
       "mean       0.612834      0.716102      3.803054    201.050337      3.498233   \n",
       "std        0.248631      0.171169      1.232592     49.943099      1.460136   \n",
       "min        0.090000      0.360000      2.000000     96.000000      2.000000   \n",
       "25%        0.440000      0.560000      3.000000    156.000000      3.000000   \n",
       "50%        0.640000      0.720000      4.000000    200.000000      3.000000   \n",
       "75%        0.820000      0.870000      5.000000    245.000000      4.000000   \n",
       "max        1.000000      1.000000      7.000000    310.000000     10.000000   \n",
       "\n",
       "               有无工伤     过去五年是否有升职          是否离职  \n",
       "count  14999.000000  14999.000000  14999.000000  \n",
       "mean       0.144610      0.021268      0.238083  \n",
       "std        0.351719      0.144281      0.425924  \n",
       "min        0.000000      0.000000      0.000000  \n",
       "25%        0.000000      0.000000      0.000000  \n",
       "50%        0.000000      0.000000      0.000000  \n",
       "75%        0.000000      0.000000      0.000000  \n",
       "max        1.000000      1.000000      1.000000  "
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5D0774C4364A4FAB868C4FE26E947290",
    "jupyter": {},
    "mdEditEnable": false,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "source": [
    "### 热力图相关性分析\n",
    "运用热力图分析各数值之间的相关性，分析结果如下：\n",
    "- 满意度(satisfaction_level）\n",
    "\t- 员工**满意度**与离职呈较大**负相关**（-）关系，与**完成项目数量**、**工龄**也有一定的负相关性。\n",
    "- 绩效评估(last_evaluation)\n",
    "\t- **绩效评估**与**完成项目数量**和**每月平均工作时长**两个特征呈较大的**正相关**（+）关系。完成项目数量越多，平均每月工作时长越长，员工能获得更高的评价。\n",
    "\t- 但绩效评估与工资，晋升都没有什么相关性，所以员工得到了高绩效评价也不会升职加薪。\n",
    "- 离职（left）\n",
    "\t- 离职率与员工**满意度**、过去5年是否有**升职**、是否有**工伤**、**工资薪酬**呈**负相关**（-）关系。如果员工对公司不太满意，且个人未获得升职加薪的机会，那么离职的可能性会很大。\n",
    "\t- 离职率与员工的**工龄**呈较大**正相关**（+）关系。与**每月平均工作时长**、所在**工作部门**也呈一定的正相关性。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {
    "hide_input": true,
    "id": "31972320AFA24CD588066F4139875D0A",
    "jupyter": {
     "source_hidden": true
    },
    "scrolled": false,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "corr = df.corr()                 # pearson相关系数\n",
    "mask = np.zeros_like(corr)\n",
    "mask[np.tril_indices_from(mask)]=True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {
    "hide_input": true,
    "id": "A2673B6B362A41E682D49847D9B35A23",
    "jupyter": {
     "source_hidden": true
    },
    "scrolled": false,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x504 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "with sns.axes_style(\"white\"):\n",
    "    sns.set(rc={'figure.figsize':(11,7)})\n",
    "    sns.set(font=\"Microsoft YaHei\")\n",
    "    ax = sns.heatmap(corr, \n",
    "                xticklabels=True, yticklabels=True, \n",
    "                cmap='RdBu', # cmap='YlGnBu',  # 颜色\n",
    "                mask=mask,   # 使用掩码只绘制矩阵的一部分\n",
    "                fmt='.3f',     # 格式设置\n",
    "                annot=True,    # 方格内写入数据\n",
    "                linewidths=.5, # 热力图矩阵之间的间隔大小\n",
    "                vmax=.4,       # 图例中最大值\n",
    "                square = True\n",
    "                )\n",
    "\n",
    "plt.title(\"Correlation\")\n",
    "label_x = ax.get_xticklabels()\n",
    "plt.setp(label_x,rotation=45, horizontalalignment='right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "77B6DCEA465541D486C4E80FD6DD9B3C",
    "jupyter": {},
    "mdEditEnable": false,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "source": [
    "### 变量分析\n",
    "#### 公司当前员工离职与在职的比例"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 翻译名称便于操作\n",
    "df.rename(columns = {'满意度':'satisfaction_level','绩效评估':'last_evaluation','完成项目数量':'number_project','每月平均工作时长（hr）':'average_montly_hours','工龄（年）':'time_spend_company','有无工伤':'Work_accident','是否离职':'left','过去五年是否有升职':'promotion_last_5years','工作部门':'sales','工资薪酬等级':'salary'},inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {
    "hide_input": true,
    "id": "563468E450904CB0AB41635A68E5E40C",
    "jupyter": {
     "source_hidden": true
    },
    "scrolled": false,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>left_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>11428</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>3571</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index  left_count\n",
       "0      0       11428\n",
       "1      1        3571"
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "left_count = df['left'].value_counts().reset_index(name = \"left_count\")\n",
    "left_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
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    "trace=[go.Pie(\n",
    "    labels=labels,\n",
    "    values=values,\n",
    "    hole=0.6,#控制环形中心空白大小\n",
    "    pull=0.1,#控制环形不同部分之间的距离\n",
    "    hoverinfo='label+percent'       #hoverinfo属性用于控制当用户将鼠标指针放到环形图上时，显示的内容\n",
    ")]\n",
    "layout=go.Layout(\n",
    "    title='员工在职与离职的比例'\n",
    ")\n",
    "fig=go.Figure(data=trace,layout=layout)\n",
    "fig"
   ]
  },
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   "source": [
    "#### 公司员工的满意度与工龄的关系\n",
    "由图可知，员工满意度和工龄关系不大，但满意度普遍较低。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
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   "source": [
    "time_mean_satifaction = df.groupby(by = ['time_spend_company'])['satisfaction_level'].mean().reset_index(name = \"average_satisfaction\") # 取满意度的均值的"
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   "source": [
    "trace = Bar(x=time_mean_satifaction.time_spend_company, y=time_mean_satifaction.average_satisfaction, marker=dict(color = colors),)\n",
    "layout = Layout(title= \"员工满意度与员工工龄的关联\",\n",
    "                xaxis = dict(title=\"工龄（年）\"),\n",
    "                yaxis = dict(title = \"平均满意度\"),\n",
    "                )\n",
    "fig2=go.Figure(data=[trace],layout= layout)\n",
    "fig2"
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   "source": [
    "#### 公司各部门的员工离职与在职情况对比\n",
    "由图可知，sales部门离职人数最多，有1014人，其次是technical技术部门离职697人和support后勤部门的555人。"
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   "source": [
    "data = []\n",
    "left_eles = df.left.unique()\n",
    "for l in left_eles:\n",
    "    trace = Bar(x = depart_left_table[l], y = depart_left_table.index, name=('离职' if l == 1 else '在职'),orientation='h',marker=dict(color=colors[l+4]))\n",
    "    data.append(trace)\n",
    "layout = Layout(title=\"每个部门的离职员工数与在职员工数对比\", barmode=\"stack\")\n",
    "fig3=go.Figure(data= data, layout=layout)\n",
    "fig3"
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    "slideshow": {
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    "tags": []
   },
   "source": [
    "#### 公司各部门的员工的工资水平\n",
    "由图可知，销售部门（sales）低工资水平的人数最多，有2099人,其次是技术部门(technical)与后勤部门(support),分别为1372人与1146人。\n"
   ]
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>salary</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>medium</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sales</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>IT</th>\n",
       "      <td>83</td>\n",
       "      <td>609</td>\n",
       "      <td>535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RandD</th>\n",
       "      <td>51</td>\n",
       "      <td>364</td>\n",
       "      <td>372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>accounting</th>\n",
       "      <td>74</td>\n",
       "      <td>358</td>\n",
       "      <td>335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hr</th>\n",
       "      <td>45</td>\n",
       "      <td>335</td>\n",
       "      <td>359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>management</th>\n",
       "      <td>225</td>\n",
       "      <td>180</td>\n",
       "      <td>225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>marketing</th>\n",
       "      <td>80</td>\n",
       "      <td>402</td>\n",
       "      <td>376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>product_mng</th>\n",
       "      <td>68</td>\n",
       "      <td>451</td>\n",
       "      <td>383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sales</th>\n",
       "      <td>269</td>\n",
       "      <td>2099</td>\n",
       "      <td>1772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>support</th>\n",
       "      <td>141</td>\n",
       "      <td>1146</td>\n",
       "      <td>942</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>technical</th>\n",
       "      <td>201</td>\n",
       "      <td>1372</td>\n",
       "      <td>1147</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "salary       high   low  medium\n",
       "sales                          \n",
       "IT             83   609     535\n",
       "RandD          51   364     372\n",
       "accounting     74   358     335\n",
       "hr             45   335     359\n",
       "management    225   180     225\n",
       "marketing      80   402     376\n",
       "product_mng    68   451     383\n",
       "sales         269  2099    1772\n",
       "support       141  1146     942\n",
       "technical     201  1372    1147"
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "depart_salary_table = pd.crosstab(index=df['sales'], columns=df['salary'])\n",
    "depart_salary_table"
   ]
  },
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   "source": [
    "pyplt = py.offline.plot\n",
    "# Traces\n",
    "trace_1 = go.Bar(\n",
    "            x = [\"IT\", \"RandD\", \"accounting\",\"hr\",\"management\",\"marketing\",\"product_mng\",\"sales\",\"support\",\"technical\"],\n",
    "            y = [83, 51, 74, 45, 225, 80, 68, 269, 141, 201],\n",
    "            name = \"high\"\n",
    "    )\n",
    "trace_2 = go.Bar(\n",
    "            x = [\"IT\", \"RandD\", \"accounting\",\"hr\",\"management\",\"marketing\",\"product_mng\",\"sales\",\"support\",\"technical\"],\n",
    "            y = [609, 364, 358, 335, 180, 402, 451, 2099, 1146, 1372],\n",
    "            name = \"low\"\n",
    "    )\n",
    "\n",
    "trace_3 = go.Bar(\n",
    "            x = [\"IT\", \"RandD\", \"accounting\",\"hr\",\"management\",\"marketing\",\"product_mng\",\"sales\",\"support\",\"technical\"],\n",
    "            y = [535, 372, 335, 359, 225, 376, 383, 1772, 942, 1147],\n",
    "            name = \"medium\"\n",
    "    )\n",
    "trace = [trace_1, trace_2, trace_3]\n",
    "# Layout\n",
    "layout = go.Layout(\n",
    "            title = '公司各部门的员工工资情况'\n",
    "    )\n",
    "# Figure\n",
    "figure = go.Figure(data = trace, layout = layout)\n",
    "figure"
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   "source": [
    "#### 员工薪资与离职率\n",
    "由图可知，低薪员工离职率为29.7%，中等薪资的员工离职率为20.4%，高薪员工的离职率只有6.63%。"
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  {
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    "salary_left_table=pd.crosstab(index=df['salary'],columns=df['left'])"
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    "data = []\n",
    "for i in range(2):\n",
    "    trace = Bar(x=salary_left_table.index, y=salary_left_table[i],name=(\"在职\" if i ==0 else \"离职\"),marker=dict(color=colors[i+4]))\n",
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    "fig5=go.Figure(data = data,layout = layout)\n",
    "fig5"
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   "cell_type": "code",
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pyplt=py.offline.plot\n",
    "labels=['在职','离职']\n",
    "trace1=[go.Pie(\n",
    "    labels=labels,\n",
    "    values=[1155,82],\n",
    "    name='high',\n",
    "    hoverinfo='label+percent+name'       #hoverinfo属性用于控制当用户将鼠标指针放到环形图上时，显示的内容\n",
    ")]\n",
    "\n",
    "trace2=[go.Pie(\n",
    "    labels=labels,\n",
    "    values=[5144,2172],\n",
    "    name='low',\n",
    "    hoverinfo='label+percent+name'       #hoverinfo属性用于控制当用户将鼠标指针放到环形图上时，显示的内容\n",
    ")]\n",
    "\n",
    "trace3=[go.Pie(\n",
    "    labels=labels,\n",
    "    values=[5129,1317],\n",
    "    name='medium',\n",
    "    hoverinfo='label+percent+name'       #hoverinfo属性用于控制当用户将鼠标指针放到环形图上时，显示的内容\n",
    ")]\n",
    "\n",
    "#trace=[trace1,trace2,trace3]\n",
    "layout=go.Layout(\n",
    "    title='员工在职与离职的比例',width=300,height=300\n",
    ")\n",
    "fig6=go.Figure(data=trace1,layout=layout)\n",
    "fig7=go.Figure(data=trace2,layout=layout)\n",
    "fig8=go.Figure(data=trace3,layout=layout)\n",
    "fig6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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    "#### 员工过去5年的升职情况与离职对比\n",
    "由图可知，过去5年都没有升过职的员工离职率相比升过职的员工要高出很多，大多数升过职的员工都在职。"
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    "promotion_dict = {0:\"没有升职\",1:\"升过职\"}\n",
    "data = []\n",
    "for i in range(2):\n",
    "    trace = Bar(x=promotion_left_table.index, y=promotion_left_table[i],name=(\"在职\" if i ==0 else \"离职\"),marker=dict(color=colors[i+4]))\n",
    "    data.append(trace)\n",
    "layout = Layout(title=\"员工过去5年是否升职对离职的影响\")\n",
    "fig9=go.Figure(data = data,layout = layout)\n",
    "fig9"
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  {
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    "slideshow": {
     "slide_type": "slide"
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    "tags": []
   },
   "source": [
    "#### 员工绩效与离职的对比\n",
    "离职的员工中也有许多绩效评估成绩优异的"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
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   "source": [
    "eva_left_table = pd.crosstab(index=df['last_evaluation'], columns=df['left'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
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   "source": [
    "data = []\n",
    "for i in range(2):\n",
    "    trace = Bar(x=eva_left_table.index, y=eva_left_table[i],name=(\"在职\" if i ==0 else \"离职\"),marker=dict(color=colors[i+2]))\n",
    "    data.append(trace)\n",
    "layout = Layout(title=\"员工的绩效评估对离职的影响\")#xaxis = dict(tickmode=\"array\",tickvals=list(promotion_dict.keys()),ticktext=list(promotion_dict.values())))\n",
    "fig10=go.Figure(data = data,layout = layout)\n",
    "fig10"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 员工每月平均工作时长与离职的对比\n",
    "过长的工作时间促使员工离职"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [],
   "source": [
    "hours_left_table = pd.crosstab(index=df['average_montly_hours'], columns=df['left'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {
    "scrolled": true
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    "data = []\n",
    "for i in range(2):\n",
    "    trace = Scatter(x=hours_left_table.index, y=hours_left_table[i],name=(\"在职\" if i ==0 else \"离职\"),marker=dict(color=colors[i+5]))\n",
    "    data.append(trace)\n",
    "layout = Layout(title=\"每月平均工作时长对离职的影响\")# xaxis = dict(tickmode=\"array\",tickvals=list(hours_dict.keys()),ticktext=list(hours_dict.values()))\n",
    "fig11=go.Figure(data = data,layout = layout)\n",
    "fig11"
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    "## 总结\n",
    "\n",
    "### 离职原因分析\n",
    "\n",
    "综上，**离职员工的特征**有以下几点：\n",
    "\n",
    "- 员工对公司**满意度普遍较低**；\n",
    "- 平均**每天工作时长**太长，工作辛苦劳累；\n",
    "- **薪资**大多为中低水平；\n",
    "- 过去5年基本**没有升过职**；\n",
    "- 离职员工大部分来自**销售**、**技术**与**后勤**部门，销售部门占比较大；\n",
    "- 员工离职有时并**不是因为绩效不好**，有一大批离职员工的绩效评价都很高。结合相关性分析的发现，高绩效并没有带来相应的升职加薪，这也从侧面反映了为什么许多高绩效员工离职的原因。\n",
    "\n",
    "**所以，公司里大部分员工离职是因为满意度低、工作时间长、薪资报酬低、没有升职的机会看不到个人发展前景等等。**\n",
    "\n",
    "### 公司需要考虑的问题\n",
    "- 为什么员工对公司满意度低？\n",
    "- 为什么高绩效员工离职率高？为什么他们没有得到相应的升职和加薪？\n",
    "- 为什么销售、技术、后勤部门离职率高？\n",
    "- 多长的工作时长能够更好的兼顾公司的工作效率和员工的工作精力？"
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